Journal articles: 'Generative System'

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Coorey, Benjamin P., and Julie R. Jupp. "Generative spatial performance design system." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28, no. 3 (July 22, 2014): 277–83. http://dx.doi.org/10.1017/s0890060414000225.

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AbstractArchitectural spatial design is a wicked problem that can have a multitude of solutions for any given brief. The information needed to resolve architectural design problems is often not readily available during the early conceptual stages, requiring proposals to be evaluated only after an initial solution is reached. This “solution-driven” design approach focuses on the generation of designs as a means to explore the solution space. Generative design can be achieved computationally through parametric and algorithmic processes. However, utilizing a large repertoire of organiational patterns and design precedent knowledge together with the precise criteria of spatial evaluation can present design challenges even to an experienced architect. In the implementation of a parametric design process lies an opportunity to supplement the designer's knowledge with computational decision support that provides real-time spatial feedback during conceptual design. This paper presents an approach based on a generative multiperformance framework, configured for generating and optimizing architectural designs based on a precedent design. The system is constructed using a parametric modeling environment enabling the capture of precedent designs, extraction of spatial analytics, and demonstration of how populations can be used to drive the generation and optimization of alternate spatial solutions. A pilot study implementing the complete workflow of the system is used to illustrate the benefits of coupling parametric modeling with structured precedent analysis and design generation.

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Zhang, Gexiang, G. Samdanielthompson, N. Gnanamalar David, Atulya K. Nagar, and K. G. Subramanian. "A Bio-Inspired Model of Picture Array Generating P System with Restricted Insertion Rules." Applied Sciences 10, no. 22 (November 23, 2020): 8306. http://dx.doi.org/10.3390/app10228306.

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In the bio-inspired area of membrane computing, a novel computing model with a generic name of P system was introduced around the year 2000. Among its several variants, string or array language generating P systems involving rewriting rules have been considered. A new picture array model of array generating P system with a restricted type of picture insertion rules and picture array objects in its regions, is introduced here. The generative power of such a system is investigated by comparing with the generative power of certain related picture array grammar models introduced and studied in two-dimensional picture language theory. It is shown that this new model of array P system can generate picture array languages which cannot be generated by many other array grammar models. The theoretical model developed is for handling the application problem of generation of patterns encoded as picture arrays over a finite set of symbols. As an application, certain floor-design patterns are generated using such an array P system.

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Mahajan, Bhushan, Deven waykar, Aditya Kadam, Vaishnavi Bhagde, and Prof S. R. Nalamwar. "Enhancing the Concept of Generative Art." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 1586–92. http://dx.doi.org/10.22214/ijraset.2023.48877.

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Abstract: Art that was entirely or partially produced by an autonomous system is referred to as generative art. An autonomous system is typically a non-human entity that is capable of making judgments on its own about aspects of a piece of art that would otherwise require direct input from the artist. We have created a GUI which lists the applications which are based on the concept of generative art. Our GUI contains applications like random password generation, abstract artworks, map generation, wallpaper generation. Password generator will provide randomly generated passwords which would be derived from the images created using image generation algorithms/scripts such as EPWT and python’s in-built random function which uses algorithms like mersenne twister, a pseudo-random number generator. This application aims to provide usefulness for old hashing algorithms which are getting obsolete day by day. Abstract artwork generator will generate random images for stimulating creativity of artists, designers, architectural and marketing firms for getting started on a new project. This generator uses general complex functions which contain scripts for different shapes and combinations. This approach is able to generate some innovative solutions and demonstrates the power of computational approach. A layout of the area with the requested coordinates and different constraints will be provided through map generating model. Wallpaper generation model will generate aesthetically pleasing wallpapers with adjustable features.

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Chen, Zhong Hong. "Biomarker Geochemistry and Hydrocarbon Generation Potential of the Evaporites in Dongying Lacustrine Basin." Advanced Materials Research 616-618 (December 2012): 1042–47. http://dx.doi.org/10.4028/www.scientific.net/amr.616-618.1042.

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To investigate hydrocarbon potential of the evaporites, some deep wells such as Haoke-1 well and Fengshen-2 well were intensively cored, tested by TOC, Rock-Eval, and chromatography and mass spectrometry and evaluated using geochemistry of biomarker and hydrocarbon generation. High content of gammacerane and low Pr/Ph was exhibited in the evaporite system compared to the non-evaporite system. Different response of biomarkers parameters for the different sedimentary systems was exhibited, such as C19/(C19+C23) terpanes, C29/(C27+C28+C29) steranes, C24/C23 and C22/C21 tricyclic terpane. The evaporites and mud stones have the capacity to generate and expel hydrocarbons. The tested samples were mostly typeⅠand typeⅡ1 of organic matter, and their original generating capacity can reach 40 mg/g rock and 20 mg/g rock respectively. The efficiency of hydrocarbon expulsion reached 60%, but the distribution of organic matter and its generative potential was highly variable. In general, the mudstones show greater generative potential than the evaporites. High maturity severely reduced the capacity of their rocks to generate and expel petroleum.

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ECKERT, CLAUDIA, IAN KELLY, and MARTIN STACEY. "Interactive generative systems for conceptual design: An empirical perspective." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, no. 4 (September 1999): 303–20. http://dx.doi.org/10.1017/s089006049913405x.

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This paper argues from extensive research findings in design psychology and industrial design processes, as well as our own observations, that interactive generative systems can be powerful tools for human designers. Moreover, interactive generative systems can fit naturally into human design thinking and industrial design practice. This discussion is focused on aesthetic design fields like knitwear and graphic design, but is largely applicable to major branches of engineering. Human designers and generative systems have complementary abilities. Humans are extremely good at perceptual evaluation of designs, according to criteria that are extremely hard to program. As a result, they can provide fitness evaluations for evolutionary generative systems. They can also tailor the biases that generation systems use to reach useful solutions quickly. We discuss an application of these approaches: Kelly's evolutionary systems for color scheme design. Automatic design systems can work interactively with human designers by generating complete designs from partial specifications, that can then be used as starting points for designing by modification. We discuss an application of this approach: Eckert's garment shape design system.

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Sankar, Meena Parvathy, and N. G. David. "Parallel Communicating String - Graph P System." Mapana - Journal of Sciences 10, no. 2 (November 25, 2011): 63–74. http://dx.doi.org/10.12723/mjs.19.6.

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The concept of parallel communicating grammar systems generating string languages is extended to string-graph P systems and their generative power is studied. It is also established that for every language L generated by a parallel communicating grammar system there exists an equivalent parallel communicating string-graph P system generating the string-graph language corresponding to L.

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MAZUROWSKI, �ukasz. "Algorithmic composition transformational-generative system for background music generation." PRZEGL�D ELEKTROTECHNICZNY 1, no. 2 (February 5, 2015): 114–19. http://dx.doi.org/10.15199/48.2015.02.27.

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Eigenfeldt, Arne. "Generating Structure – Towards Large-Scale Formal Generation." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 10, no. 5 (June 29, 2021): 2–9. http://dx.doi.org/10.1609/aiide.v10i5.12764.

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Metacreative systems have been very successful at generating event-to-event musical details and generating short forms. However, large-scale formal decisions have tended to remain in the hands of a human, either through an operator guiding an improvisational system along in performance, or the assemblage of shorter generated sections into longer forms. This paper will describe the open problem of large-scale generative form, and the author’s attempts at delegating such decisions to a metacreative system by describing several of his generative systems and their approach to structure. The author will describe in greater detail his latest system — The Indifference Engine — and how it negotiates between agent intentions and performance information derived from a live performer.

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Zhang, Xiang, and Qiang Yang. "Transfer Hierarchical Attention Network for Generative Dialog System." International Journal of Automation and Computing 16, no. 6 (October 16, 2019): 720–36. http://dx.doi.org/10.1007/s11633-019-1200-0.

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Abstract In generative dialog systems, learning representations for the dialog context is a crucial step in generating high quality responses. The dialog systems are required to capture useful and compact information from mutually dependent sentences such that the generation process can effectively attend to the central semantics. Unfortunately, existing methods may not effectively identify importance distributions for each lower position when computing an upper level feature, which may lead to the loss of information critical to the constitution of the final context representations. To address this issue, we propose a transfer learning based method named transfer hierarchical attention network (THAN). The THAN model can leverage useful prior knowledge from two related auxiliary tasks, i.e., keyword extraction and sentence entailment, to facilitate the dialog representation learning for the main dialog generation task. During the transfer process, the syntactic structure and semantic relationship from the auxiliary tasks are distilled to enhance both the word-level and sentence-level attention mechanisms for the dialog system. Empirically, extensive experiments on the Twitter Dialog Corpus and the PERSONA-CHAT dataset demonstrate the effectiveness of the proposed THAN model compared with the state-of-the-art methods.

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Bengu, Golgen, and Jorge Haddock. "A generative simulation-optimization system." Computers & Industrial Engineering 10, no. 4 (January 1986): 301–13. http://dx.doi.org/10.1016/0360-8352(86)90016-1.

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Shadmand, Sahar, and Arzu Ozen Yavuz. "Trial of generative system regarding forming shape via benefiting nature." International Journal of Academic Research 7, no. 3 (May 30, 2015): 551–59. http://dx.doi.org/10.7813/2075-4124.2015/7-3/a.80.

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Ma, Longxuan, Mingda Li, Wei-Nan Zhang, Jiapeng Li, and Ting Liu. "Unstructured Text Enhanced Open-Domain Dialogue System: A Systematic Survey." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–44. http://dx.doi.org/10.1145/3464377.

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Incorporating external knowledge into dialogue generation has been proven to benefit the performance of an open-domain Dialogue System (DS), such as generating informative or stylized responses, controlling conversation topics. In this article, we study the open-domain DS that uses unstructured text as external knowledge sources ( U nstructured T ext E nhanced D ialogue S ystem ( UTEDS )). The existence of unstructured text entails distinctions between UTEDS and traditional data-driven DS and we aim at analyzing these differences. We first give the definition of the UTEDS related concepts, then summarize the recently released datasets and models. We categorize UTEDS into Retrieval and Generative models and introduce them from the perspective of model components. The retrieval models consist of Fusion, Matching, and Ranking modules, while the generative models comprise Dialogue and Knowledge Encoding, Knowledge Selection (KS), and Response Generation modules. We further summarize the evaluation methods utilized in UTEDS and analyze the current models’ performance. At last, we discuss the future development trends of UTEDS, hoping to inspire new research in this field.

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Smorzhenkov, Nikita, and Elena Ignatova. "The use of generative design for the architectural solutions synthesis in the typical construction of residential buildings." E3S Web of Conferences 281 (2021): 04008. http://dx.doi.org/10.1051/e3sconf/202128104008.

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The study deals with the application of generative design technology in solving architectural and construction problems. Generative design is a powerful tool for creating design choices. It is necessary to formalize the generation process, set the necessary parameters, algorithms and restrictions. The purpose of the study is to determine the possibilities of the generative design complex application in the typical construction of residential buildings. It is proposed to use the structure, floor, and section of the building as the structural modules for generating variants of buildings. In turn, module variants can be created based on generative design. It is concluded that it is possible to create a whole system of structural and parametric synthesis of architectural solutions.

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Anderson, Christopher, Arne Eigenfeldt, and Philippe Pasquier. "The Generative Electronic Dance Music Algorithmic System (GEDMAS)." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, no. 5 (June 30, 2021): 5–8. http://dx.doi.org/10.1609/aiide.v9i5.12649.

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The Generative Electronic Dance Music Algorithmic System (GEDMAS) is a generative music system that composes full Electronic Dance Music (EDM) compositions. The compositions are based on a corpus of transcribed musical data collected through a process of detailed human transcription. This corpus data is used to analyze genre-specific characteristics associated with EDM styles. GEDMAS uses probabilistic and first order Markov chain models to generate song form structures, chord progressions, melodies and rhythms. The system is integrated with Ableton Live, and allows its user to select one or several songs from the corpus, and generate a 16 tracks/parts composition in a few clicks.

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Hassani, Hossein, Roozbeh Razavi-Far, Mehrdad Saif, and Vasile Palade. "Generative Adversarial Network-Based Scheme for Diagnosing Faults in Cyber-Physical Power Systems." Sensors 21, no. 15 (July 30, 2021): 5173. http://dx.doi.org/10.3390/s21155173.

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This paper presents a novel diagnostic framework for distributed power systems that is based on using generative adversarial networks for generating artificial knockoffs in the power grid. The proposed framework makes use of the raw data measurements including voltage, frequency, and phase-angle that are collected from each bus in the cyber-physical power systems. The collected measurements are firstly fed into a feature selection module, where multiple state-of-the-art techniques have been used to extract the most informative features from the initial set of available features. The selected features are inputs to a knockoff generation module, where the generative adversarial networks are employed to generate the corresponding knockoffs of the selected features. The generated knockoffs are then fed into a classification module, in which two different classification models are used for the sake of fault diagnosis. Multiple experiments have been designed to investigate the effect of noise, fault resistance value, and sampling rate on the performance of the proposed framework. The effectiveness of the proposed framework is validated through a comprehensive study on the IEEE 118-bus system.

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Nguyen, Dat Tien, Tuyen Danh Pham, Ganbayar Batchuluun, Kyoung Jun Noh, and Kang Ryoung Park. "Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network." Sensors 20, no. 7 (March 25, 2020): 1810. http://dx.doi.org/10.3390/s20071810.

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Although face-based biometric recognition systems have been widely used in many applications, this type of recognition method is still vulnerable to presentation attacks, which use fake samples to deceive the recognition system. To overcome this problem, presentation attack detection (PAD) methods for face recognition systems (face-PAD), which aim to classify real and presentation attack face images before performing a recognition task, have been developed. However, the performance of PAD systems is limited and biased due to the lack of presentation attack images for training PAD systems. In this paper, we propose a method for artificially generating presentation attack face images by learning the characteristics of real and presentation attack images using a few captured images. As a result, our proposed method helps save time in collecting presentation attack samples for training PAD systems and possibly enhance the performance of PAD systems. Our study is the first attempt to generate PA face images for PAD system based on CycleGAN network, a deep-learning-based framework for image generation. In addition, we propose a new measurement method to evaluate the quality of generated PA images based on a face-PAD system. Through experiments with two public datasets (CASIA and Replay-mobile), we show that the generated face images can capture the characteristics of presentation attack images, making them usable as captured presentation attack samples for PAD system training.

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Duda, Jan. "Generative System for Manufacturing Process Planning." Communications - Scientific letters of the University of Zilina 2, no. 1 (March 31, 2000): 10–16. http://dx.doi.org/10.26552/com.c.2000.1.10-16.

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Mazzone, Marco. "A Generative System for Intentional Action?" Topoi 33, no. 1 (September 13, 2013): 77–85. http://dx.doi.org/10.1007/s11245-013-9186-7.

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Chen, Lu, Feifei Lee, Hanqing Chen, Wei Yao, Jiawei Cai, and Qiu Chen. "Automatic Chinese Font Generation System Reflecting Emotions Based on Generative Adversarial Network." Applied Sciences 10, no. 17 (August 28, 2020): 5976. http://dx.doi.org/10.3390/app10175976.

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Manual font design is difficult and requires professional knowledge and skills to perform. Therefore, how to automatically generate the required fonts is a very challenging research task. On the other hand, there are few people who have studied the relationship between fonts and emotions, and common fonts generally cannot reflect emotional information. This paper proposes an Emotional Guidance GAN: an automatic Chinese font generation framework based on Generative Adversarial Network (GAN), which enables the generated fonts to reflect human emotional information. First, an elaborated questionnaire system was developed from Tencent company, which aims to quantitatively figure out the relationship between fonts and emotions. A visual expression recognition part is designed based on the trained model to provide a font generation module with conditional information. Moreover, the Emotional Guidance GAN (EG-GAN) with EM Distance and Gradient Penalty, as well as classification strategies, is proposed to generate new fonts with combined multiple styles that infer by an expression recognition module. The results of the evaluation experiments and the resolution of the synthesized font characters show the credibility of our model.

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Min, Jun, Zhaoqi Liu, Lei Wang, Dongyang Li, Maoqing Zhang, and Yantai Huang. "Music Generation System for Adversarial Training Based on Deep Learning." Processes 10, no. 12 (November 27, 2022): 2515. http://dx.doi.org/10.3390/pr10122515.

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With the rapid development of artificial intelligence, the application of this new technology to music generation has attracted more attention and achieved gratifying results. This study proposes a method for combining the transformer deep-learning model with generative adversarial networks (GANs) to explore a more competitive music generation algorithm. The idea of text generation in natural language processing (NLP) was used for reference, and a unique loss function was designed for the model. The training process solves the problem of a nondifferentiable gradient in generating music. Compared with the problem that LSTM cannot deal with long sequence music, the model based on transformer and GANs can extract the relationship in the notes of long sequence music samples and learn the rules of music composition well. At the same time, the optimized transformer and GANs model has obvious advantages in the complexity of the system and the accuracy of generating notes.

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Lin, Zhaojiang, Peng Xu, Genta Indra Winata, Farhad Bin Siddique, Zihan Liu, Jamin Shin, and Pascale Fung. "CAiRE: An End-to-End Empathetic Chatbot." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (April 3, 2020): 13622–23. http://dx.doi.org/10.1609/aaai.v34i09.7098.

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We present CAiRE, an end-to-end generative empathetic chatbot designed to recognize user emotions and respond in an empathetic manner. Our system adapts the Generative Pre-trained Transformer (GPT) to empathetic response generation task via transfer learning. CAiRE is built primarily to focus on empathy integration in fully data-driven generative dialogue systems. We create a web-based user interface which allows multiple users to asynchronously chat with CAiRE. CAiRE also collects user feedback and continues to improve its response quality by discarding undesirable generations via active learning and negative training.

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Navarro-Cáceres, María, Wataru Hashimoto, Sara Rodríguez-González, Belén Pérez-Lancho, and Juan Corchado. "Sensoring a Generative System to Create User-Controlled Melodies." Sensors 18, no. 10 (September 21, 2018): 3201. http://dx.doi.org/10.3390/s18103201.

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The automatic generation of music is an emergent field of research that has attracted the attention of countless researchers. As a result, there is a broad spectrum of state of the art research in this field. Many systems have been designed to facilitate collaboration between humans and machines in the generation of valuable music. This research proposes an intelligent system that generates melodies under the supervision of a user, who guides the process through a mechanical device. The mechanical device is able to capture the movements of the user and translate them into a melody. The system is based on a Case-Based Reasoning (CBR) architecture, enabling it to learn from previous compositions and to improve its performance over time. The user uses a device that allows them to adapt the composition to their preferences by adjusting the pace of a melody to a specific context or generating more serious or acute notes. Additionally, the device can automatically resist some of the user’s movements, this way the user learns how they can create a good melody. Several experiments were conducted to analyze the quality of the system and the melodies it generates. According to the users’ validation, the proposed system can generate music that follows a concrete style. Most of them also believed that the partial control of the device was essential for the quality of the generated music.

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Wang, Jiachun, Junkui Song, Yizhe Zhang, and Hao Chen. "Design of 3D Display System for Intangible Cultural Heritage Based on Generative Adversarial Network." Scientific Programming 2022 (July 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/2944750.

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This paper designs a three-dimensional display system for intangible cultural heritage based on generative adversarial networks. The system function is realized through four modules: input module, data processing module, 3D model generation module, and model output module. Two 3D model reconstruction methods are used to realize the transformation from 2D images to 3D models. In the low-resolution Nuo surface 3D construction, multiresidual dense blocks are introduced and applied to the deep image super-resolution network. The experimental comparison results show that the quadratic optimization multifusion 3D construction model proposed in this paper can achieve considerable improvement and can improve the reconstruction accuracy by about 6.3%. In the high-resolution 3D construction of the Nuo surface, a generative adversarial network model is used to improve the generator, discriminator, and loss function of the original SRGAN model. Experimental results show that this method can generate super-resolution images with more realistic and natural depth maps. In addition, when it is used for high-resolution 3D Nuo surface sculpting, it can also generate 3D voxel Nuo surfaces with more details.

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Macedo, J. "A generative system for re-engineering manufacturing system organization." International Journal of Production Research 37, no. 12 (August 1999): 2639–64. http://dx.doi.org/10.1080/002075499190455.

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Ntintakis, Ioannis, and George E. Stavroulakis. "Progress and recent trends in generative design." MATEC Web of Conferences 318 (2020): 01006. http://dx.doi.org/10.1051/matecconf/202031801006.

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Due to recent developments in the field of additive manufacturing enormous advantages have become in product design and manufacturing process. Before the appearance of additive manufacturing, developing very complex or light weight structures was difficult to manufacture. The development of artificial intelligent technology helps to develop new collaborative tools and algorithms. Generative design approach is one of them. The outcome model from a generative design study is not depending only from designer/engineer experience or his knowledge. Designers can react with sophisticated algorithms through CAD programs to specify the shape and the topology of the model. A significant tool on a generative design system is topology optimization which is able to generate different solutions. The changes in design process are significant. A rough conceptual design (sketch) or a 3d model is first prepared. Then, boundary conditions, safety factor, manufacturing limitations and materials properties are defined. The generative design system generates potential solutions. It’s up to the designer to find the design that best fits to his need. In this paper the review covers the limitations of current systems through the study of specific design cases using commercial generative design systems.

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Meshkov, V. G., and D. E. Iskra. "Structural-Parametric Model of the Design System." Proceedings of the Southwest State University 24, no. 4 (February 4, 2021): 244–55. http://dx.doi.org/10.21869/2223-1560-2020-24-4-244-255.

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Purpose of research. In the design process developers dealing with professional issues related to the development of new design methods and tools inevitably face problems associated with the modeling of automated and designed objects. The analysis of the problems of designing complex systems have shown that the disadvantages of the design process appear, in particular, due to the incomplete generation of possible project options, as well as their partial ordering. At the same time, there is a certain imbalance in the properties of design systems and the problem of the lack of adequate methods for describing design processes arises. Methods. In the process of partial ordering of acceptable project options, when determining the structure of the design system at the generation stage, the structure of some generative grammar is used, which should have, first of all, control properties that provide a partial ordering of options already in the generation process. In addition, to take into account changes in the composition and parameters of design solutions, the generative grammar must have adaptive properties, which determines the need to choose an appropriate method for adaptive control of the generation process, taking into account, for example, the frequent repetition of the design process. Results. A model of a formalized recognition system is determined when choosing a project variant presented in the class of recognizing grammars. Rules for the formation of elements of recognizing grammars are given, and the alphabet can be represented by a multi-alternative probabilistic network of design options. The choice of the design object type is considered, the elements of the design system structure are determined. Conclusion. The structure of the design system can be parametrically determined based on the rules for generating elements of recognizing grammars, and the possibilities for generating variants can be coordinated with its recognizing capabilities, while the choice of the structure and forecasting of project parameters is made taking into account the number and properties of design resources. It should be noted that providing only the properties of the design system that reduce the description of the process can lead to difficulties in recognizing variants. At the same time, the choice of an adaptive design system structure allows you to build a language system with variable properties that provide the required reduction or expansion of the project description.

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Yu, Zhenming, Zhenyu Ju, Xinlei Zhang, Ziyi Meng, Feifei Yin, and Kun Xu. "High-speed multimode fiber imaging system based on conditional generative adversarial network." Chinese Optics Letters 19, no. 8 (2021): 081101. http://dx.doi.org/10.3788/col202119.081101.

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ISNAINI, Mohammad M., Ryuta SATO, and Keiichi SHIRASE. "1906 Generative Machining Process Planning System Based on Total Removal Volume Concept." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2015.8 (2015): _1906–1_—_1906–5_. http://dx.doi.org/10.1299/jsmelem.2015.8._1906-1_.

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Singh, Montek, Utkarsh Bajpai, Vijayarajan V., and Surya Prasath. "Generation of fashionable clothes using generative adversarial networks." International Journal of Clothing Science and Technology 32, no. 2 (August 6, 2019): 177–87. http://dx.doi.org/10.1108/ijcst-12-2018-0148.

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Purpose There are various style options available when one buys clothes on online shopping websites, however the availability the new fashion trends or choices require further user interaction in generating fashionable clothes. The paper aims to discuss this issue. Design/methodology/approach Based on generative adversarial networks (GANs) from the deep learning paradigm, here the authors suggest model system that will take the latest fashion trends and the clothes bought by users as input and generate new clothes. The new set of clothes will be based on trending fashion but at the same time will have attributes of clothes where were bought by the consumer earlier. Findings In the proposed machine learning based approach, the clothes generated by the system will personalized for different types of consumers. This will help the manufacturing companies to come up with the designs, which will directly target the customer. Research limitations/implications The biggest limitation of the collected data set is that the clothes in the two domains do not belong to a specific category. For instance the vintage clothes data set has coats, dresses, skirts, etc. These different types of clothes are not segregated. Also there is no restriction on the number of images of each type of cloth. There can many images of dresses and only a few for the coats. This can affect the end results. The aim of the paper was to find whether new and desirable clothes can be created from two different domains or not. Analyzing the impact of “the number of images for each class of cloth” is something which is aim to work in future. Practical implications The authors believe such personalized experience can increase the sales of fashion stores and here provide the feasibility of such a clothes generation system. Originality/value Applying GANs from the deep learning models for generating fashionable clothes.

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Pradana, Harly Yoga. "Konsep Garap Karawitan dalam Sudut Pandang Musik Generatif." INVENSI 6, no. 2 (December 16, 2021): 91–107. http://dx.doi.org/10.24821/invensi.v6i2.5275.

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ABSTRAK Seni generatif merupakan seni yang menitikberatkan pada sistem, aturan, dan kondisi awal sebagai pancingan untuk kemudian berkembang dengan sendirinya. Seni generatif semakin banyak disoroti seiring dengan perkembangan teknologi. Akibatnya, pandangan terhadap seni generatif semakin mengacu pada penggunaan komputer atau peranti berteknologi tinggi (hi-tech). Karawitan merupakan produk budaya masyarakat Jawa pada wilayah seni suara yang diasumsikan memiliki sifat generatif. Tulisan ini membahas tentang cara pandang konsep garap karawitan Jawa dari sudut pandang musik generatif. Beberapa teknik dan aspek garap dibahas dan dimaknai sebagai logika kerja. Metode yang digunakan adalah studi kepustakaan dengan menerapkan teknik segmentasi dalam menganalisis data. Hasil dari pembahasan ini diketahui bahwa aspek generatif pada karawitan terdiri dari empat jenis: struktur pola, gramatika, kompleksitas, kondisi, dan aturan (rule). Diketahui pula logika kerja dari aspek tersebut yang berkaitan dengan wilayah musik dan harapannya bisa dikembangkan untuk membangun sistem pada karya musik generatif secara umum. The Concept of "Garap" on Javanese Karawitan in The Generative Music Perspective ABSTRACT Generative art is about using systems, rules, and initial conditions as a trigger to develop independently in the creative process. Technology has changed the perspective of generative art nowadays. Generative art is more identified with innovative methods that use computers or high-tech devices. "Karawitan Jawa" is a cultural product of Javanese society in the auditive field, which is assumed to be generative. This paper discusses the concept of "garap" in Javanese karawitan from the generative music perspective. Several techniques and aspects of "garap" are discussed and interpreted as logical procedures. This research uses the literature study method by applying the segmentation technique. As a result, it was found that there are four generative aspects in karawitan: pattern structure, grammar, complexity, rule conditions. It is also known that these aspects are related to music, and hopefully, this will be useful for building a generative music system in general.

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Janssen, Patrick H. T., John H. Frazer, and Ming-Xi Tang. "A Framework for Generating and Evolving Building Designs." International Journal of Architectural Computing 3, no. 4 (December 2005): 449–70. http://dx.doi.org/10.1260/147807705777781112.

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This paper describes a comprehensive framework for generative evolutionary design. The key problem that is identified is generating alternative designs with an appropriate level of variability. Within the proposed framework, the design process is split into two phases: in the first phase, the design team develops and encodes the essential and identifiable character of the designs to be generated and evolved; in the second phase, the design team uses an evolutionary system to generate and evolve designs that embody this character. This approach allows design variability to be carefully controlled. In order to verify the feasibility of the proposed framework, a generative process capable of generating controlled variability is implemented and demonstrated.

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Dalal, Dhaval, Muhammad Bilal, Hritik Shah, Anwarul Islam Sifat, Anamitra Pal, and Philip Augustin. "Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models." Energies 16, no. 4 (February 7, 2023): 1636. http://dx.doi.org/10.3390/en16041636.

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Generation of realistic scenarios is an important prerequisite for analyzing the reliability of renewable-rich power systems. This paper satisfies this need by presenting an end-to-end model-free approach for creating representative power system scenarios on a seasonal basis. A conditional recurrent generative adversarial network serves as the main engine for scenario generation. Compared to prior scenario generation models that treated the variables independently or focused on short-term forecasting, the proposed implicit generative model effectively captures the cross-correlations that exist between the variables considering long-term planning. The validity of the scenarios generated using the proposed approach is demonstrated through extensive statistical evaluation and investigation of end-application results. It is shown that analysis of abnormal scenarios, which is more critical for power system resource planning, benefits the most from cross-correlated scenario generation.

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Li, Guangyu, Bo Jiang, Hao Zhu, Zhengping Che, and Yan Liu. "Generative Attention Networks for Multi-Agent Behavioral Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7195–202. http://dx.doi.org/10.1609/aaai.v34i05.6209.

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Understanding and modeling behavior of multi-agent systems is a central step for artificial intelligence. Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identifies agent groups and interaction types. Built upon advances in deep generative models and a novel attention mechanism, our model can learn interactions in highly heterogeneous systems with linear complexity in the number of agents. We apply this model to three multi-agent systems in different domains and evaluate performance on a diverse set of tasks including behavior prediction, interaction analysis and system identification. Experimental results demonstrate its ability to model multi-agent systems, yielding improved performance over competitive baselines. We also show the model can successfully identify agent groups and interaction types in these systems. Our model offers new opportunities to predict complex multi-agent behaviors and takes a step forward in understanding interactions in multi-agent systems.

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Xue, Yuan, Yuan-Chen Guo, Han Zhang, Tao Xu, Song-Hai Zhang, and Xiaolei Huang. "Deep image synthesis from intuitive user input: A review and perspectives." Computational Visual Media 8, no. 1 (October 27, 2021): 3–31. http://dx.doi.org/10.1007/s41095-021-0234-8.

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AbstractIn many applications of computer graphics, art, and design, it is desirable for a user to provide intuitive non-image input, such as text, sketch, stroke, graph, or layout, and have a computer system automatically generate photo-realistic images according to that input. While classically, works that allow such automatic image content generation have followed a framework of image retrieval and composition, recent advances in deep generative models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and flow-based methods have enabled more powerful and versatile image generation approaches. This paper reviews recent works for image synthesis given intuitive user input, covering advances in input versatility, image generation methodology, benchmark datasets, and evaluation metrics. This motivates new perspectives on input representation and interactivity, cross fertilization between major image generation paradigms, and evaluation and comparison of generation methods.

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Dharna, Aaron, Julian Togelius, and L. B. Soros. "Co-Generation of Game Levels and Game-Playing Agents." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, no. 1 (October 1, 2020): 203–9. http://dx.doi.org/10.1609/aiide.v16i1.7431.

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Open-endedness, a longstanding cornerstone of artificial life research, is the ability of systems to generate potentially unbounded ontologies of increasing novelty and complexity. Engineering generative systems displaying at least some degree of this ability is a goal with clear applications to procedural content generation in games. The Paired Open-Ended Trailblazer (POET) algorithm, heretofore explored only in a biped walking domain, is a coevolutionary system that simultaneously generates environments and agents that can solve them. This paper introduces a POET-Inspired Neuroevolutionary System for KreativitY (PINSKY) in games, which co-generates levels for multiple video games and agents that play them. This system leverages the General Video Game Artificial Intelligence (GVGAI) framework to enable co-generation of levels and agents for the 2D Atari-style games Zelda and Solar Fox. Results demonstrate the ability of PINSKY to generate curricula of game levels, opening up a promising new avenue for research at the intersection of procedural content generation and artificial life. At the same time, results in these challenging game domains highlight the limitations of the current algorithm and opportunities for improvement.

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Liu, Hua, Jinglun Ren, Jianfei Lyu, Xueying Lyu, and Yuelin Feng. "Hydrocarbon source rock evaluation of the Lower Cretaceous system in the Baibei Depression, Erlian Basin." Energy Exploration & Exploitation 36, no. 3 (December 22, 2017): 355–72. http://dx.doi.org/10.1177/0144598717748761.

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The K1s, K1d, K1t, and K1a Formations are potential source rock intervals for hydrocarbon formation, all of which are part of the Lower Cretaceous system in the Baibei Depression in the Erlian Basin in China. However, no well has found oil flow because the hydrocarbon-generating potential of the source rocks has not been comprehensively evaluated. Based on organic geochemical and petrological analyses, all the source rocks possess highly variable total organic carbon and S1 + S2 contents. Total organic carbon and S1 + S2 contents indicate that the K1a2 Formation through the K1d1 Formation are source rocks that have fair to good generative potential and the K1d2 Formation through the K1s Formation are source rocks that have good to very good generative potential. The organic matter in the K1a2 Formation is dominated by Type I and II kerogen; thus, it is considered to be oil prone based on H/C versus O/C plots. Most of the analyzed samples were deposited in reducing environments and sourced from marine algae; thus, they are oil prone. However, only two source rock intervals were thermally mature with vitrinite reflectance values in the required range. Hydrocarbon-generating histories show that the K1t and K1a2 intervals began to generate hydrocarbons during the depositional period of the K1d2 and K1d3 Formations, respectively, and stopped generating hydrocarbons at the end of the depositional period of the late Cretaceous. Therefore, the main stage of hydrocarbon migration and accumulation was between the depositional period of the K1d2 and K1s Formations, and the critical moment was the depositional period of the late K1s Formation. The generation conversion efficiency reached approximately 55% in the K1a2 Formation and 18% in the K1t Formation at the end of the Cretaceous sedimentary stage. In general, the effective oil traps are those reservoirs that are near the active source rock in the generating sags in the Baibei Depression.

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Monro, Gordon. "Emergence and Generative Art." Leonardo 42, no. 5 (October 2009): 476–77. http://dx.doi.org/10.1162/leon.2009.42.5.476.

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Emergence, the idea that in some sense more comes out of a system than was put in, is the holy grail of generative art. Yet emergence is a slippery concept. Originating in the philosophy of science, it has been taken up in systems theory, cognitive science and Artificial Life. As a consequence there are numerous definitions of emergence in the literature, but none well-suited to discussions of generative art. The paper reviews some existing definitions and proposes a new definition of generative-art emergence.

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Brannon, Nathan, Carl Lippitt, and Randy Stiles. "Generative Visualization System for Subject Matter Experts." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 47, no. 20 (October 2003): 2092–96. http://dx.doi.org/10.1177/154193120304702003.

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Mitchell, J. R., and A. D. Radford. "EAVE, a generative expert system for detailing." Environment and Planning B: Planning and Design 14, no. 3 (1987): 281–92. http://dx.doi.org/10.1068/b140281.

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Jerne, Niels K. "The generative grammar of the immune system." Bioscience Reports 5, no. 6 (June 1, 1985): 439–51. http://dx.doi.org/10.1007/bf01116941.

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Jerne, N. "The generative grammar of the immune system." Science 229, no. 4718 (September 13, 1985): 1057–59. http://dx.doi.org/10.1126/science.4035345.

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JERNE, NIELS K. "The Generative Grammar of the Immune System." Scandinavian Journal of Immunology 38, no. 1 (July 1993): 2–8. http://dx.doi.org/10.1111/j.1365-3083.1993.tb01687.x.

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Jerne, N. K. "The generative grammar of the immune system." EMBO Journal 4, no. 4 (April 1985): 847–52. http://dx.doi.org/10.1002/j.1460-2075.1985.tb03709.x.

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Kwieciński, Krystian, and Jan Słyk. "Interactive generative system supporting participatory house design." Automation in Construction 145 (January 2023): 104665. http://dx.doi.org/10.1016/j.autcon.2022.104665.

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Chao, Fei, Gan Lin, Ling Zheng, Xiang Chang, Chih-Min Lin, Longzhi Yang, and Changjing Shang. "An LSTM Based Generative Adversarial Architecture for Robotic Calligraphy Learning System." Sustainability 12, no. 21 (October 31, 2020): 9092. http://dx.doi.org/10.3390/su12219092.

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Robotic calligraphy is a very challenging task for the robotic manipulators, which can sustain industrial manufacturing. The active mechanism of writing robots require a large sized training set including sequence information of the writing trajectory. However, manual labelling work on those training data may cause the time wasting for researchers. This paper proposes a machine calligraphy learning system using a Long Short-Term Memory (LSTM) network and a generative adversarial network (GAN), which enables the robots to learn and generate the sequences of Chinese character stroke (i.e., writing trajectory). In order to reduce the size of the training set, a generative adversarial architecture combining an LSTM network and a discrimination network is established for a robotic manipulator to learn the Chinese calligraphy regarding its strokes. In particular, this learning system converts Chinese character stroke image into the trajectory sequences in the absence of the stroke trajectory writing sequence information. Due to its powerful learning ability in handling motion sequences, the LSTM network is used to explore the trajectory point writing sequences. Each generation process of the generative adversarial architecture contains a number of loops of LSTM. In each loop, the robot continues to write by following a new trajectory point, which is generated by LSTM according to the previously written strokes. The written stroke in an image format is taken as input to the next loop of the LSTM network until the complete stroke is finally written. Then, the final output of the LSTM network is evaluated by the discriminative network. In addition, a policy gradient algorithm based on reinforcement learning is employed to aid the robot to find the best policy. The experimental results show that the proposed learning system can effectively produce a variety of high-quality Chinese stroke writing.

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Cui, Bo, Guyue Hu, and Shan Yu. "DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1175–83. http://dx.doi.org/10.1609/aaai.v35i2.16204.

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An important challenge for neural networks is to learn incrementally, i.e., learn new classes without catastrophic forgetting. To overcome this problem, generative replay technique has been suggested, which can generate samples belonging to learned classes while learning new ones. However, such generative models usually suffer from increased distribution mismatch between the generated and original samples along the learning process. In this work, we propose DeepCollaboration (D-Collab), a collaborative framework of deep generative and discriminative models to solve this problem effectively. We develop a discriminative learning model to incrementally update the latent feature space for continual classification. At the same time, a generative model is introduced to achieve conditional generation using the latent feature distribution produced by the discriminative model. Importantly, the generative and discriminative models are connected through bidirectional training to enforce cycle-consistency of mappings between feature and image domains. Furthermore, a domain alignment module is used to eliminate the divergence between the feature distributions of generated images and real ones. This module together with the discriminative model can perform effective sample mining to facilitate incremental learning. Extensive experiments on several visual recognition datasets show that our system can achieve state-of-the-art performance.

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Wei-Jian Hu, Wei-Jian Hu, Tang-Ying Xie Wei-Jian Hu, Bao-Shan Li Tang-Ying Xie, Yong-Xing Du Bao-Shan Li, and Neal N. Xiong Yong-Xing Du. "An Edge Intelligence-based Generative Data Augmentation System for IoT Image Recognition Tasks." 網際網路技術學刊 22, no. 4 (July 2021): 765–78. http://dx.doi.org/10.53106/160792642021072204005.

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Hornby, Gregory S., and Jordan B. Pollack. "Creating High-Level Components with a Generative Representation for Body-Brain Evolution." Artificial Life 8, no. 3 (July 2002): 223–46. http://dx.doi.org/10.1162/106454602320991837.

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One of the main limitations of scalability in body-brain evolution systems is the representation chosen for encoding creatures. This paper defines a class of representations called generative representations, which are identified by their ability to reuse elements of the genotype in the translation to the phenotype. This paper presents an example of a generative representation for the concurrent evolution of the morphology and neural controller of simulated robots, and also introduces GENRE, an evolutionary system for evolving designs using this representation. Applying GENRE to the task of evolving robots for locomotion and comparing it against a non-generative (direct) representation shows that the generative representation system rapidly produces robots with significantly greater fitness. Analyzing these results shows that the generative representation system achieves better performance by capturing useful bias from the design space and by allowing viable large scale mutations in the phenotype. Generative representations thereby enable the encapsulation, coordination, and reuse of assemblies of parts.

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Sinelnikova, T. I., and N. A. Shvetsova. "REALIZATION OF SYSTEM SCIENCE METHODS OF EPISTEMOLOGICAL LEVEL OF GENERATIVE SYSTEMS." Scientific and Technical Volga region Bulletin 6, no. 5 (October 2016): 209–11. http://dx.doi.org/10.24153/2079-5920-2016-6-5-209-211.

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Martens, Chris, Adam Summerville, Michael Mateas, Joseph Osborn, Sarah Harmon, Noah Wardrip-Fruin, and Arnav Jhala. "Proceduralist Readings, Procedurally." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 12, no. 2 (June 25, 2021): 53–59. http://dx.doi.org/10.1609/aiide.v12i2.12892.

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While generative approaches to game design offer great promise, systems can only reliably generate what they can “understand,” often limited to what can be handencoded by system authors. Proceduralist readings, a way of deriving meaning for games based on their underlying processes and interactions in conjunction with aesthetic and cultural cues, offer a novel, systematic approach to game understanding. We formalize proceduralist argumentation as a logic program that performs static reasoning over game specifications to derive higher-level meanings (e.g., deriving dynamics from mechanics), opening the door to broader and more culturally-situated game generation.

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Journal articles on the topic 'Generative System'

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